Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning
Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.
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Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning
Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.
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Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning

Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning

by Mohsen Nady
Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning

Statistics with R for Machine Learning: Volume 2 Advanced Resampling Techniques with R for Machine Learning

by Mohsen Nady

Overview

Resampling techniques are key to improving model performance and reliability in machine learning. This volume explores advanced resampling methods, including cross-validation, bootstrapping, and hyperparameter tuning, using R. Readers will learn how to apply these techniques to optimize model accuracy and prevent overfitting. Practical examples and case studies illustrate their real-world applications. This voulme is an essential resource for data scientists and machine learning enthusiasts aiming to master resampling strategies.

Product Details

ISBN-13: 9781779564719
Publisher: Arcler Press
Publication date: 01/10/2025
Pages: 185
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Mohsen Nady is a pharmacist with a M.D. in Microbiology and a Diploma in Industrial Pharmacy. Besides, Mohsen has more than 10 years of experience in Statistics and Data Analytics. Mohsen has applied his skills to different projects related to Genomics, Microbiology, Biostatistics, Six Sigma, Data Analytics, Data Visualization, Building Apps, Geography, Market Analysis, Business Analysis, Machine Learning, etc. Mohsen also published his thesis in a high-impact journal that attracted many citations, where all the statistical analyses were performed by him in addition to the methodological part. Furthermore, Mohsen has earned different certificates, from top universities (Harvard, Johns Hopkins, Denmark, etc) in Statistics, Data Analytics, Data Visualization, and Machine Learning that highlight his outstanding diverse skills.

Table of Contents

Chapter 1 Stratified Resampling Chapter 2 Time-Based Resampling
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